This paper reports an intercomparison study on undisturbed trade wind cumulus convection under steadystate conditions as observed during the Barbados Oceanographic and Meteorological Experiment (BOMEX) with 10 large eddy simulation (LES) models. A main objective of this study is to obtain a quantitative assessment of the quality of the turbulent dynamics for this type of boundary layer clouds as produced by the different LES codes. A 6-h simulation shows excellent model-to-model agreement of the observed vertical thermodynamical structure, reasonable agreement of variances and turbulent fluxes, and good agreement of quantities conditionally sampled within the model clouds, such as cloud cover, liquid water, and cloud updraft strength. In the second part of this paper the LES dataset is used to evaluate simple models that are used in parameterizations of current general circulation models (GCMs). Finally, the relation of this work to subsequent LES studies of more complicated regimes is discussed, and guidance is given for the design of future observational studies of shallow cumulus boundary layers.
The analysis of possible regional climate changes over Europe as simulated by ten regional climate models within the context of PRUDENCE requires a careful investigation of possible systematic biases in the models. The purpose of this paper is to identify how the main model systematic biases vary across the different models.Two fundamental aspects of model validation are addressed here: the ability to simulate i) the longterm (30 or 40 years) mean climate and ii) the inter-annual variability. The analysis concentrates on near-surface air temperature and precipitation over land and focuses mainly on winter and summer.In general, there is a warm bias with respect to the CRU data set in these extreme seasons and a tendency to cold biases in the transition seasons. In winter the typical spread (standard deviation) between the models is 1K. During summer there is generally a better agreement between observed and simulated values of inter-annual variability although there is a relatively clear signal that the modeled temperature variability is larger than suggested by observations, while precipitation variability is closer to observations. The areas with warm (cold) bias in winter generally exhibit wet (dry) biases, whereas the relationship is the reverse during summer (though much less clear, coupling warm (cold) biases with dry (wet) ones). When comparing the RCMs with their driving GCM, they generally reproduce the large-scale circulation of the GCM though in some cases there are substantial differences between regional biases in surface temperature and precipitation.4
The fifth intercomparison of the Global Water and Energy Experiment Cloud System Studies Working Group 1 is used as a vehicle for better understanding the dynamics of trade wind cumuli capped by a strong inversion. The basis of the intercomparison is 10 simulations by 7 groups. These simulations are supplemented by many further sensitivity studies, including some with very refined grid meshes. The simulations help illustrate the turbulent dynamics of trade cumuli in such a regime. In many respects the dynamics are similar to those found in many previous simulations of trade cumuli capped by weaker inversions. The principal differences are the extent to which the cloud layer is quasi-steady in the current simulations, evidence of weak countergradient momentum transport within the cloud layer, and the development and influence of an incipient stratiform cloud layer at the top of the cloud layer. Although many elements of the turbulent structure (including the wind profiles, the evolution of cloud-base height, the statistics of the subcloud layer, and the nature of mixing in the lower and middle parts of the cloud layer) are robustly predicted, the representation of the stratiform cloud amount by the different simulations is remarkably sensitive to a number of factors. Chief among these are differences between numerical algorithms. These sensitivities persist even among simulations on relatively refined grid meshes. Part of this sensitivity is attributed to a physically realistic positive radiative feedback, whereby a propensity toward higher cloud fractions in any given simulation is amplified by longwave radiative cooling. The simulations also provide new insight into the dynamics of the transition layer at cloud base. In accord with observations, the simulations predict that this layer is most identifiable in terms of moisture variances and gradients. The simulations help illustrate the highly variable (in both height and thickness) nature of the transition layer, and we speculate that this variability helps regulate convection. Lastly the simulations are used to help evaluate simple models of trade wind boundary layers. In accord with previous studies, mass-flux models well represent the dynamics of the cloud layer, while mixing-length models well represent the subcloud layer. The development of the stratiform cloud layer is not, however, captured by the mass-flux models. The simulations indicate that future theoretical research needs to focus on interface rules, whereby the cloud layer is coupled to the subcloud layer below and the free atmosphere above. Future observational studies of this regime would be of most benefit if they could provide robust cloud statistics as a function of mean environmental conditions.
The capability of a set of 7 coordinated regional climate model simulations performed in the framework of the CLARIS-LPB Project in reproducing the mean climate conditions over the South American continent has been evaluated. The model simulations were forced by the ERA-Interim reanalysis dataset for the period 1990–2008 on a grid resolution of 50 km, following the CORDEX protocol. The analysis was focused on evaluating the reliability of simulating mean precipitation and surface air temperature, which are the variables most commonly used for impact studies. Both the common features and the differences among individual models have been evaluated and compared against several observational datasets. In this study the ensemble bias and the degree of agreement among individual models have been quantified. The evaluation was focused on the seasonal means, the area-averaged annual cycles and the frequency distributions of monthly means over target sub-regions. Results show that the Regional Climate Model ensemble reproduces adequately well these features, with biases mostly within ±2 °C and ±20 % for temperature and precipitation, respectively. However, the multi-model ensemble depicts larger biases and larger uncertainty (as defined by the standard deviation of the models) over tropical regions compared with subtropical regions. Though some systematic biases were detected particularly over the La Plata Basin region, such as underestimation of rainfall during winter months and overestimation of temperature during summer months, every model shares a similar behavior and, consequently, the uncertainty in simulating current climate conditions is low. Every model is able to capture the variety in the shape of the frequency distribution for both temperature and precipitation along the South American continent. Differences among individual models and observations revealed the nature of individual model biases, showing either a shift in the distribution or an overestimation or underestimation of the range of variability
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